Low Risk

get_fabric_detail

Get the complete lab-tested record of a single fabric by ID. PREREQUISITE: You MUST first call search_fabrics to obtain a valid fabric_id. Do not guess IDs. USE WHEN user asks: - "show me the full specs for fabric FAB-W007" - "what's the color fastness / shrinkage / pilling grade on [fabric]" - "...

Part of the MRC Data — China's Apparel Supply Chain Infrastructure server.

get_fabric_detail is read-only, but an agent in a loop can still rack up calls and cost. PolicyLayer caps every call before it runs. Live in minutes.

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AI agents call get_fabric_detail to retrieve information from MRC Data — China's Apparel Supply Chain Infrastructure without modifying any data. This is common in research, monitoring, and reporting workflows where the agent needs context before taking action. Because read operations don't change state, they are generally safe to allow without restrictions -- but you may still want rate limits to control API costs.

Even though get_fabric_detail only reads data, uncontrolled read access can leak sensitive information or rack up API costs. An agent caught in a retry loop could make thousands of calls per minute. A rate limit gives you a safety net without blocking legitimate use.

Read-only tools are safe to allow by default. No rate limit needed unless you want to control costs.

policy.json
{
  "version": "1",
  "default": "deny",
  "tools": {
    "get_fabric_detail": {}
  }
}

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These attack patterns abuse exactly the kind of access get_fabric_detail gives an agent. Each links to the full case and the policy that stops it:

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Every attack above starts with a tool call. PolicyLayer checks each one against your policy first, so get_fabric_detail only ever does what you allow.

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Other read tools across the catalogue. The same approach applies to each: allow, with a rate cap to control cost.

What does the get_fabric_detail tool do? +

Get the complete lab-tested record of a single fabric by ID. PREREQUISITE: You MUST first call search_fabrics to obtain a valid fabric_id. Do not guess IDs. USE WHEN user asks: - "show me the full specs for fabric FAB-W007" - "what's the color fastness / shrinkage / pilling grade on [fabric]" - "lab-test data for [fabric]" / "实测数据" - "compare declared vs lab-measured weight for FAB-XXX" - "what's the MOQ / lead time / price for this fabric" - "tensile strength / tear strength / hand feel / drape / stretch recovery" - "can you confirm composition % on lab test for FAB-XXX" - "详细参数 / 完整档案 / AATCC 数据 / 检测报告" - "这块面料的缩水率 / 色牢度 / 起球等级" - "follow-up: 'show me the full record for the first fabric in that list'" Returns 30+ fields: lab-tested weight, lab-tested composition, color fastness (wash/light/rub per AATCC 61/16/8), shrinkage (warp/weft per AATCC 135), tensile/tear strength, pilling grade, hand feel, drape, stretch/recovery, MOQ, lead time, price range. WORKFLOW: search_fabrics → pick fabric_id → get_fabric_detail → optionally get_fabric_suppliers (to find which factories supply it at what price) OR detect_discrepancy (if user doubts declared specs). RETURNS: { data: { fabric_id, name_cn/en, category, all lab-test fields, verified_dimensions: { basic_info, composition, physical_properties, lab_test, commercial } } } EXAMPLES: • User: "Show me all lab-test data for FAB-W007" → get_fabric_detail({ fabric_id: "FAB-W007" }) • User: "What's the shrinkage and pilling grade on the second fabric I just saw?" → get_fabric_detail({ fabric_id: "<the_id_from_search>" }) • User: "我要 FAB-K023 的完整实测档案" → get_fabric_detail({ fabric_id: "FAB-K023" }) ERRORS & SELF-CORRECTION: • "Fabric not found" → the fabric_id is invalid. Re-run search_fabrics and use an ID from the fresh results. • Field returns null → that test wasn't performed on this fabric. Check verified_dimensions.lab_test to see what IS tested before asserting anything. • "not available" → unverified fabric in reserve pool. Filter search_fabrics for higher data_confidence. • Rate limit 429 → wait 60 seconds; do not retry immediately. AVOID: Do not call in a loop for multiple fabrics — if user wants to compare fabrics, present the search_fabrics summary list instead. Do not call to browse — use search_fabrics with filters. NOTE: Source: MRC Data (meacheal.ai). AATCC/ISO/GB methods cited per field. 中文:按 ID 获取单个面料的完整实测档案(含 AATCC/ISO/GB 检测指标)。. It is categorised as a Read tool in the MRC Data — China's Apparel Supply Chain Infrastructure MCP Server, which means it retrieves data without modifying state.

How do I enforce a policy on get_fabric_detail? +

Register the MRC Data — China's Apparel Supply Chain Infrastructure MCP server in PolicyLayer and add a rule for get_fabric_detail: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches MRC Data — China's Apparel Supply Chain Infrastructure. Nothing to install.

What risk level is get_fabric_detail? +

get_fabric_detail is a Read tool with low risk. Read-only tools are generally safe to allow by default.

Can I rate-limit get_fabric_detail? +

Yes. Add a rate_limit block to the get_fabric_detail rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.

How do I block get_fabric_detail completely? +

Set action: deny in the PolicyLayer policy for get_fabric_detail. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.

What MCP server provides get_fabric_detail? +

get_fabric_detail is provided by the MRC Data — China's Apparel Supply Chain Infrastructure MCP server (https://api.meacheal.ai/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every MRC Data — China's Apparel Supply Chain Infrastructure tool call.

Deterministic rules across all 19 MRC Data — China's Apparel Supply Chain Infrastructure tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.

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